Relation of gait measures with mild unilateral knee pain during walking using machine learning

Multicenter Osteoarthritis Study Investigators

Research output: Contribution to journalArticlepeer-review

Abstract

Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis (OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-m walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique (“super learning”) to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain. Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR 0.80 (0.64–1.00) and aOR 0.79 (0.66–0.95), respectively].

Original languageEnglish
Article number22200
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - 1 Dec 2022

Funding

FundersFunder number
AG19069 University of California at San Francisco IRBFWA00000068
HIPAAIRB FWA00000301, 45 CFR 164.514
National Institutes of Health
U.S. Department of Health and Human Services
National Institute on AgingU01AG018832
National Institute of Arthritis and Musculoskeletal and Skin DiseasesP30 AR0702571, K01 AR06972
Rheumatology Research Foundation
Boston University
University of Alabama at BirminghamIRB FWA00005960
University of IowaIRB FWA00003007
University of Alabama
Ministry of Science and Technology, Taiwan

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